Original Article
Obesity
EPIDEMIOLOGY/GENETICS
Genome-Wide Association Study of Height-Adjusted BMI in Childhood Identifies Functional Variant in ADCY3 Evangelia Stergiakouli1, Romy Gaillard2,3,4, Jeremy M. Tavar e5, Nina Balthasar6, Ruth J. Loos7, Hendrik R. Taal2,3,4, 1,8 3,9 e G. Uitterlinden3,9, John P. Kemp1,8,12, David M. Evans , Fernando Rivadeneira , Beate St Pourcain1,10,11, Andr 3 1,12 13 2,3,4 , George Davey Smith1 and Albert Hofman , Susan M. Ring , Tim J. Cole , Vincent W.V. Jaddoe 1 Nicholas J. Timpson
Objective: Genome-wide association studies (GWAS) of BMI are mostly undertaken under the assumption that “kg/m2” is an index of weight fully adjusted for height, but in general this is not true. The aim here was to assess the contribution of common genetic variation to a adjusted version of that phenotype which appropriately accounts for covariation in height in children. Methods: A GWAS of height-adjusted BMI (BMI[x] 5 weight/heightx), calculated to be uncorrelated with height, in 5809 participants (mean age 9.9 years) from the Avon Longitudinal Study of Parents and Children (ALSPAC) was performed. Results: GWAS based on BMI[x] yielded marked differences in genomewide results profile. SNPs in ADCY3 (adenylate cyclase 3) were associated at genome-wide significance level (rs11676272 (0.28 kg/m3.1 change per allele G (0.19, 0.38), P 5 6 3 1029). In contrast, they showed marginal evidence of association with conventional BMI [rs11676272 (0.25 kg/m2 (0.15, 0.35), P 5 6 3 1027)]. Results were replicated in an independent sample, the Generation R study. Conclusions: Analysis of BMI[x] showed differences to that of conventional BMI. The association signal at ADCY3 appeared to be driven by a missense variant and it was strongly correlated with expression of this gene. Our work highlights the importance of well understood phenotype use (and the danger of convention) in characterising genetic contributions to complex traits. Obesity (2014) 22, 2252–2259. doi:10.1002/oby.20840
Introduction BMI (weight(kg)/height(m2)) has become a uniformly used measure of weight given height despite being defined in the 19th century based only on population specific knowledge at the time (1). As an index of weight for height it ought to be uncorrelated with height, but in practice it is not. This complicates its biological interpretation
as the correlation between BMI and height varies across different age groups, body types and ethnicities (2,3). Different power terms [x] for height are required in the calculation of BMI in men and women and across different age groups and ethnicities to achieve maximum correlation with total body fat measured by Dual-energy X-ray absorptiometry (DXA) and minimum correlation with height
1
MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK. Correspondence: Nicholas J. Timpson (
[email protected]) 2 The Generation R Study Group, Erasmus Medical Center, Rotterdam, The Netherlands 3 Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands 4 Department of Paediatrics, Erasmus Medical Center, Rotterdam, The Netherlands 5 School of Biochemistry, University of Bristol, Bristol, UK 6 School of Physiology and Pharmacology, University of Bristol, Bristol, UK 7 The Charles Bronfman Institute of Personalize Medicine, The Mindich Child Health and Development, The Icahn School of Medicine at Mount Sinai, New York, USA 8 University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia 9 Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands 10 School of Oral and Dental Sciences, University of Bristol, Bristol, UK 11 School of Experimental Psychology, University of Bristol, Bristol, UK 12 Avon Longitudinal Study of Parents and Children (ALSPAC), School of Social and Community Medicine, University of Bristol, Bristol, UK 13 Population, Policy and Practice Programme, UCL Institute of Child Health, London, UK Funding agencies: The UK Medical Research Council and the Wellcome Trust (Grant ref: 092731) and the University of Bristol provide core support for ALSPAC. This work was supported by the Medical Research Council MC_UU_12013/1-9. TJC was funded by Medical Research Council grant MR/J004839/1. Disclosure: The authors report no conflict of interest. Author Contribution: N.J.T., G.D.S., V.W.V.J., R.G. and E.S. designed the project. N.J.T., G.D.S., V.W.V., A.H., A.G.U., F.R. and H.R.T. supervised the research. E.S., R.G., N.B. and N.J.T. performed analysis. D.M.E., B.S.P., J.P.K., S.M.R. and N.J.T. performed genotyping and data preparation. E.S., R.G., J.M.T., R.J.F.L., G.D.S. and N.J.T. wrote the manuscript. J.M.T., N.B., R.J.F.L., H.R.T., D.M.E., F.R., B.S.P., A.G.U., A.H., T.J.C., V.W.V.J., G.D.S. and N.J.T. contributed to data interpretation and reviewed the manuscript for intellectual content. Additional Supporting Information may be found in the online version of this article. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Received: 4 April 2014; Accepted: 26 June 2014; Published online 21 July 2014. doi:10.1002/oby.20840
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Obesity | VOLUME 22 | NUMBER 10 | OCTOBER 2014
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Original Article
Obesity
EPIDEMIOLOGY/GENETICS
(4). In addition, BMI is considered a measure of adiposity although it is particularly inaccurate for measuring adiposity in individuals with elevated lean body mass, such as athletes (5). BMI has been found previously not to be appropriate for children later in childhood, as the formula of weight(kg)/height(m2)) overestimates the BMI of tall or physically advanced children (6). Dividing BMI by the appropriate power of height as a function of the child’s age was suggested as the best way to assess adiposity in children (6). However, the genetic profiles of BMI versus suggested alternative measurements of adiposity in children have not been assessed. Phenotypic refinement is important in the undertaking of informative and well powered GWAS. Not only can the use of more biologically proximal measurements reduce the level of noise associated with any given genetic association signal, but the redefinition of a routine measurement can yield marked differences in genetic profile. A clear example of this was seen in the publication of the now well-known association between variation at FTO and fat mass (7,8). Initially, FTO was discovered as a type 2 diabetes (T2D) locus, as reported in an association study for T2D in the absence of BMI matching in cases and controls (9). The combination of study design and phenotypic refinement allowed for the demonstration that FTO was exerting an indirect effect on T2D risk through its relationship with BMI (7,8). Concerning anthropometry, the assessment of genome-wide contributions of common variants to waist-to-hip ratio (WHR) and WHR adjusted for BMI are examples of association studies where relatively simple anthropometric measurements have been refined through either subtype or adjustment and have yielded novel genomewide association profiles (10,11). Although BMI was designed to assess weight independent of height, it remains correlated with height owing to its generalized derivation. This correlation changes throughout the life course and has the potential to complicate inference and reduce power in association studies. Targeting this well-known, but often ignored, limitation in BMI as a measure for fat mass we aimed to assess the contribution of common genetic variation to a height-adjusted version of that phenotype which appropriately accounts for covariation in height in children. Using data available from the Avon Longitudinal Study of Parents and Children (ALSPAC) study, we set out to undertake a genome-wide association study (GWAS) for a height-adjusted version of BMI using the appropriate power function for minimizing the correlation between BMI and height in the age group with the largest correlation between weight and height.
Methods ALSPAC ALSPAC is a prospective birth cohort which recruited pregnant women with expected delivery dates between April 1991 and December 1992 from Bristol UK. About 14,541 pregnant women were initially enrolled with 14,062 children born. Detailed information on health and development of children and their parents were collected from regular clinic visits and completion of questionnaires. A detailed description of the cohort has been published previously (12,13). Ethical approval was obtained from the ALSPAC Law and Ethics Committee and the Local Ethics Committees. Please note that the study website contains details of all the data that is available through a fully searchable data dictionary (http://www.bris.ac.uk/ alspac/researchers/data-access/data-dictionary/).
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A total of 9912 participants were genotyped using the Illumina HumanHap550 quad genome-wide SNP genotyping platform. Quality control assessment and imputation are described in Supporting Information. After quality control assessment and imputation the data set consisted of 8365 individuals 2,608,006 SNPs available for analysis.
Generation R The Generation R Study is a population-based prospective cohort study of pregnant women and their children from fetal life onwards in Rotterdam, The Netherlands (16,17). All children were born between April 2002 and January 2006, and currently followed until young adulthood. Of all eligible children in the study area, 61% were participating in the study at birth. Anthropometric data were collected in several developmental stages. Cord blood samples including DNA have been collected at birth. The current study used the first set of Generation R samples of Northern European Ancestry. Samples were genotyped using Illumina Infinium II HumanHap610 Quad Arrays following standard manufacturer’s protocols. Quality control assessment and imputation are described in Supporting Information. After quality control assessment and imputation 2729 children and 2,543,887 SNPs were included in the analyses.
Phenotype calculation For ALSPAC, we used measurements of height and weight at clinic visits when the children were nine years of age to calculate BMI. Height was measured to the last complete mm using the Harpenden Stadiometer. A total of 5809 unrelated children had both anthropometric and genetic data appropriate for our analysis. Their mean age was 9.9 years (SD 0.3), the mean BMI was 17.7 (SD 2.8), mean height was 139.5 cm (SD 6.3) and 50.5% were female. A Lunar prodigy narrow fan beam densitometer was used to perform a whole body DXA (Dual-energy Xray absorptiometry) scan where bone content, lean and fat masses are measured (18,19). BMI was also measured as above in Generation R at regular intervals with the latest measurement used for this analysis [mean age 5 6.1 years (SD 0.4)]. A total of 2089 unrelated children had both anthropometric and genetic data appropriate for our analysis with mean BMI 15.9 (SD 1.4), mean height 119.5 cm (SD 5.6). In ALSPAC, we defined height-adjusted BMI as BMI[x] 5 weight(kg)/ height(m)x). For each age group we calculated BMI[x] iteratively increasing the power [x] term by 0.1 each time. We then measured the correlation of each BMI[x] with height (within children of the same age group) based on Pearson’s correlation coefficient. We selected the power term that resulted in the lowest correlation coefficient of BMI[x] with height for any given age. This approach yielded a value for BMI[x] which performed equivalently to adjusting log BMI for log height (6), but which reported the appropriate power term for each age group and thus the relative inefficiency of conventional BMI. In addition, we calculated zBMI, which is a commonly used measure in clinical practice, by standardising BMI by age and sex. Stata 12 was used for the calculations (20). In Generation R BMI was calculated as BMI 5 weight(kg)/height(m)2) and height was included as a covariate in the GWAS model.
Statistical methods Genomewide association analyses were performed using MACH2QTL V110 (14,15). To investigate if the association at the adenylate cyclase 3 (ADCY3) locus could be attributed to rs11676272,
Obesity | VOLUME 22 | NUMBER 10 | OCTOBER 2014
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Obesity
GWAS of Height-Adjusted BMI Stergiakouli et al.
imputed genome-wide SNP data were available. We used linear regression to investigate the association between rs11676272 and any transcript within 500 kb of this SNP. Genevar, a database and Java application for the analysis and visualization of SNP-gene associations in eQTL studies (23), was used to test for evidence of ADCY3 expression in public databases. ADCY3 expression was analyzed in data from 856 healthy female twins of the Multiple Tissue Human Expression Resource (MuTHER) resource in both adipose and lymphoblastoid cell lines (24).
Results
Figure 1 Power term [x] required for the least correlation of BMI[x] 5 weight/(height)x with height. This is reflected by the correlation coefficient across different age groups in all children from the Avon Longitudinal Study of Parents and Children (ALSPAC) study and stratified by sex. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
conditional analysis was performed by including the rs11676272 dose generated during imputation in the regression model and conducting regional single marker association analyses with BMI[3.1]. Age, sex, height, and rs11676272 dose were included as covariates in the model. Plots were generated using LocusZoom (21). For the meta-analysis of BMI adjusted for height in ALSPAC and Generation R samples, SNPs that had a minor allele frequency
